CN104713197A - Central air conditioning system optimizing method and system based on mathematic model - Google Patents
Central air conditioning system optimizing method and system based on mathematic model Download PDFInfo
- Publication number
- CN104713197A CN104713197A CN201510082203.4A CN201510082203A CN104713197A CN 104713197 A CN104713197 A CN 104713197A CN 201510082203 A CN201510082203 A CN 201510082203A CN 104713197 A CN104713197 A CN 104713197A
- Authority
- CN
- China
- Prior art keywords
- energy consumption
- mathematical modeling
- fan coil
- value
- water
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/10—Temperature
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/20—Humidity
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2140/00—Control inputs relating to system states
- F24F2140/20—Heat-exchange fluid temperature
Abstract
A central air conditioning system optimizing method based on a mathematic model includes the following steps of changing values of main variables within constraints on the basis of the established energy consumption mathematic model so that the total energy consumption of a system can reach the minimum, wherein at the moment, the parameter value of the system is the optimal working condition value; adjusting the main variables to the optimal working condition value. Meanwhile, the invention provides the central air conditioning system optimizing system based on the mathematic model. The system comprises a processing module and a dispatching module. The processing module changes the values of the main variables in the constraints on the basis of the established energy consumption mathematic model so that the total energy consumption of the system can reach the minimum, and at the moment, the parameter value of the system is the optimal working condition value. The dispatching module is used for adjusting the main variables to the optimal working condition value. By means of the method and system, running parameters of a central air conditioning system can be optimized, the central air conditioning system runs under the optimal working condition, and electric energy is saved.
Description
Technical field
The application relates to air-conditioning technical field, especially a kind of central air conditioner system optimization method based on Mathematical Modeling and system.
Background technology
Along with the progress of science and the development of society, the requirement of people to quality of life is more and more higher, all trades and professions are also progressively promoting the requirement of production technology, central air-conditioning can improve life and the general level of the health of people, improve the epidemic disaster of workshop, now become one of prerequisite condition of modern architecture.
In the construction of central air conditioner system, only considered design conditions at present, but central air conditioner system only 25% running time be in design conditions, the annual overwhelming majority time is all run under being in sub-load, when load variations, unit still runs according to design conditions or arbitrarily adjusts wherein certain equipment fortune operating mode, will cause the waste of air-conditioning system electric energy.
Summary of the invention
The application provides a kind of central air conditioner system optimization method based on Mathematical Modeling, solves in prior art and can not optimize central air conditioner system operational factor, and then cause the problem of waste of energy.
According to the first aspect of the application, the application provides a kind of central air conditioner system optimization method based on Mathematical Modeling, comprises the following steps:
Based on the energy consumption Mathematical Modeling set up, in constraints, change the value of primary variables, the system total energy consumption of making reaches minimum of a value, and now, the value of systematic parameter is optimum operating condition value;
Primary variables is adjusted to described optimum operating condition value.
According to the second aspect of the application, the application provides a kind of optimization system of the central air conditioner system based on Mathematical Modeling, comprises processing module and scheduler module; Processing module is based on the energy consumption Mathematical Modeling set up, and in constraints, change the value of primary variables, the system total energy consumption of making reaches minimum of a value, and now, the value of systematic parameter is optimum operating condition value; Scheduler module is used for primary variables to be adjusted to optimum operating condition value.
The beneficial effect of the application is, the application is based on the energy consumption Mathematical Modeling set up, the value of primary variables is changed in constraints, the system total energy consumption of making reaches minimum of a value, and now, the value of systematic parameter is optimum operating condition value, then described primary variables is adjusted to described optimum operating condition value, just can optimize the operational factor of central air conditioner system, central air conditioner system be run under optimum operating condition, has saved electric energy.
Accompanying drawing explanation
Fig. 1 is the flow chart of embodiment 1;
Fig. 2 is the structural representation of the Mathematical Modeling of embodiment 2;
Fig. 3 is variable frequency pump power schematic diagram;
Fig. 4 is the efficiency curve diagram of motor and frequency converter;
Fig. 5 is the flow chart of embodiment 3;
Fig. 6 is the structural representation of embodiment 5;
Fig. 7 is the simulation result figure of the application.
Detailed description of the invention
By reference to the accompanying drawings the present invention is described in further detail below by detailed description of the invention.
Central air conditioner system, in running, affects its Variable Factors run various, and for the multivariable air-conditioning system optimization of nonlinearity, too much control variables can cause model solution difficulty, also can produce adverse influence to optimum results.Thus to consider various factors, simplify the selection of control variables as far as possible, retain the variable larger to systematic influence, reject and the overall situation do not affected or affects less variable, like this can simplified model, the stability of model can be kept again.By to after the Mathematical Modeling of each parts and analysis on Operating, the variable obtained involved by air-conditioning system mainly can be divided into two large classes: a class is performance variable, namely can measure controllable independent variable in system; Another kind of is disturbance variable, i.e. uncontrollable variable in system, the application be namely based on to the control of independent variable to reach the object of optimization.
Embodiment 1:
Based on a central air conditioner system optimization method for Mathematical Modeling, as shown in Figure 1, comprise the following steps:
S101: based on the energy consumption Mathematical Modeling set up, change the value of primary variables in constraints, the system total energy consumption of making reaches minimum of a value, now, the value of systematic parameter is optimum operating condition value;
S102: primary variables is adjusted to described optimum operating condition value.
Wherein, energy consumption Mathematical Modeling comprises the Mathematical Modeling of the Mathematical Modeling of system total energy consumption and each molecular system of construction system total energy consumption.These Mathematical Modelings comprise multiple variable, and primary variables refers in performance variable needs the main variable considered, these variablees by people for presetting.Constraints refers to the excursion of primary variables under reasonable terms.Based on this, the value of above-mentioned variable is changed in constraints, be brought in the energy consumption Mathematical Modeling of foundation, the system total energy consumption of making reaches minimum of a value, now system reaches optimum power consumption state, and the value of system parameters reaches optimum operating condition value, then primary variables is adjusted to optimum operating condition value, make system works under optimum power consumption state, just save electric energy.
Embodiment 2:
As the improvement of embodiment 1, as shown in Figure 2.Because the total energy consumption of central air conditioner system produces primarily of handpiece Water Chilling Units, water pump, cooling tower and blower fan comb, therefore, the Mathematical Modeling built comprises handpiece Water Chilling Units energy consumption model 10, pump energy consumption model 20, cooling tower energy consumption model 30 and fan coil energy consumption model 40, and determines the primary variables affecting above-mentioned Mathematical Modeling: cooling water leaving water temperature
, cold in-water temperature
, chilled-water flow
, the air quantity of fan coil under applying working condition
with the mass flow of air
, the energy consumption that above-mentioned handpiece Water Chilling Units energy consumption model 10, pump energy consumption model 20, cooling tower energy consumption model 30 and fan coil energy consumption model 40 calculate is system total energy consumption.
Wherein, the Mathematical Modeling of handpiece Water Chilling Units energy consumption is
in formula,
for handpiece Water Chilling Units wasted work rate;
for under handpiece Water Chilling Units declared working condition
;
for the input power under handpiece Water Chilling Units declared working condition.
Due to the main energy-consuming parts that water pump is in water system, other energy consumption proportions such as conduits of energy loss are little, so when building pump energy consumption Mathematical Modeling, only consider the energy consumption of water pump.Because the input power of water pump to become the relation of cube ratio with rotating speed, so water pump is energy-conservation normally by regulating its rotating speed to realize.For most central air conditioner system, generally use variable frequency pump, thus the present embodiment is mainly based on variable frequency pump founding mathematical models.Variable frequency pump forms primarily of frequency converter, motor and water pump.Frequency converter is used for regulating the rotating speed of motor, thus changes the operation energy consumption of water pump.
When setting up pump energy consumption Mathematical Modeling, need the efficiency considering frequency converter, motor and water pump.As shown in Figure 3, in figure
,
,
,
be respectively total power input, power input to machine, pump shaft power, water pump effective output,
,
,
be respectively frequency converter efficiency, electric efficiency and water pump efficiency.Usually, the target of pump energy saving mainly concentrates on pump shaft power
or water pump effective power
on, do not consider the efficiency of frequency converter and motor, therefore this energy consumption all can not embody the actual consumption of water pump, and from total energy consumption angle, the total power input of water pump could embody the actual consumption of water pump.Based on this, the present embodiment, by the method for least square curve fitting, sets up pump energy consumption Mathematical Modeling:
Wherein,
for pump energy consumption power;
for Media density, in this application, the medium related to is water, and therefore Media density is herein the Media density of water;
for acceleration of gravity;
for water pump lift;
for the flow of water pump;
for the efficiency of frequency converter;
for the efficiency of motor;
for the efficiency of water pump.
As shown in Figure 4, due to electric efficiency
not definite value, but change with the change of motor speed.According to electric efficiency computing formula:
, in formula
for the relative rotation speed of motor.Meanwhile, frequency converter efficiency calculation formula:
, make efficiency curve variation diagram, solid line is electric efficiency change, dotted line is frequency converter efficiency change, can find out after frequency conversion measure taked by water pump, even if run under rated speed, because the efficiency of motor and frequency converter is also less than 1, the total efficiency of water pump also can decline about about 5 ﹪.It can also be seen that in figure
=0.4 is a turning point of curve, when specific revolution is greater than 0.4, curvilinear motion is more steady, illustrate that motor and frequency converter efficiency decline little, but when specific revolution is less than 0.4, the decay of its efficiency is very fast, therefore advise that frequency conversion speed adjusting pump runs when specific speed is greater than 0.4, namely minimum discharge should not lower than 40 ﹪ of metered flow.
About the energy consumption model of cooling tower, the present embodiment derives by analyzing its heat-transfer mechanism the Mathematical Modeling being more suitable for engineer applied:
Wherein,
for cooling tower energy consumption;
for identification coefficient;
for the mass flow of air;
for the mass flow of cooling water;
for cooling water leaving water temperature;
for cold in-water temperature; In formula
need cooling tower test, data carry out identification by experiment.When cooling tower heat dissipation capacity, out door climatic parameter one timing, keep cooling water flow constant, then cooling water supply backwater temperature difference is certain, so can regulate blower fan of cooling tower rotational speed optimization condenser inflow temperature.(in PLSCONFM literary composition, whether the implication of all variablees correct, in literary composition the implication of all variablees be all data and the patent drafting provided is provided facility done by, the variable in literary composition is too many, please unify the title of same variable, not same variable different names)
For fan coil, set up fan coil energy consumption Mathematical Modeling:
Wherein,
for the energy consumption of fan coil,
for the energy consumption of fan coil under standard condition;
for the discharge of fan coil under applying working condition,
for the discharge of fan coil under standard condition;
for the air quantity of fan coil under applying working condition,
for the air quantity of fan coil under standard condition;
for fan coil under applying working condition air intlet wet-bulb temperature,
for fan coil air intlet wet-bulb temperature under standard condition;
for fan coil under applying working condition chilled water inlet temperature,
for fan coil chilled water inlet temperature under standard condition.
Thus obtain system total energy consumption Mathematical Modeling:
=
+
+
+
, wherein
for system total energy consumption.
Further, in above-mentioned Mathematical Modeling, the constraints of primary variables is:
,
for cooling water leaving water temperature, its minimum lower limit is the wet-bulb temperature of surrounding air, and this value is the theoretical minimum in heat transfer process.Its ceiling temperature to keep in condenser pressure in the safety requirements value of acceptable level, thus
for the wet-bulb temperature of surrounding air,
for the safety requirements value of condenser.
,
for cold in-water temperature, this value can not be too little, if the too low meeting of temperature causes chilled water to freeze, hinders chilled water flowing in the loop, so
for the critical-temperature that cooling water freezes.Its upper limit depends on two aspects: be enough refrigeration dutys that high-temperature water can meet required for air-conditioned room on the one hand; The humidity of demand fulfillment air conditioning area on the other hand, therefore
temperature for enough refrigeration dutys of meeting required for air-conditioned room and when meeting air conditioning area humidity, its concrete numerical value needs to determine in conjunction with site environment.
, for chilled-water flow
if chilled-water flow is too small, then can not meet the refrigeration duty requirement of chilled water loop in time, water temperature is caused to exceed safety value, therefore minimum flow is keep the water temperature in chilled water circuit to be in normal operation range on the one hand, on the other hand because the total efficiency of water pump is by the impact of frequency conversion, water pump minimum discharge is run under water pump also must be made to be in high efficiency; Maximum stream flow mainly limits by the ability of pump motor.By the analysis to a large amount of water pump, chilled water minimum discharge should lower than 40 ﹪ of metered flow, thus
for 40% of metered flow,
for metered flow.
, the minimum air quantity of blower fan is determined by user or system controller, and blower fan maximum quantity of wind is determined by the ability of blower motor,
for nominal air delivery;
,
for the mass flow of air, its maximum is also determined by cooling tower ability, is thus metered flow.
Embodiment 3:
As shown in Figure 5, specifically comprise the following steps:
S201: analyze energy consumption Mathematical Modeling, identifies mathematic(al) structure and the character of energy consumption Mathematical Modeling automatically;
S202: according to fixed mathematic(al) structure and character, chooses the derivation algorithm corresponding to energy consumption Mathematical Modeling;
S203: the value changing primary variables in constraints, makes system total energy consumption reach minimum of a value according to derivation algorithm;
S204: primary variables is adjusted to optimum operating condition value.
When calculating, need to carry out a series of pretreatment to Mathematical Modeling, mainly comprise: 1, the direct process of peer-to-peer constraint, constant regarded as by the variate-value that can directly decide, the constraints number of decision variable that actual needs in model solves and demand fulfillment can be reduced through such process as far as possible, improve the solution efficiency of problem; 2, the type of Optimized model is identified, after the direct process that peer-to-peer retrains completes, preprocessor is analyzed to the model of input, the mathematic(al) structure of automatic model of cognition and character, determine the type of Optimized model, thus determine which kind of derivation algorithm next step adopts, then according to derivation algorithm, bring constraints into, try to achieve final optimum operating condition value.
Embodiment 4:
As the improvement of embodiment 2, three performance curves below the energy consumption Mathematical Modeling Main Basis of handpiece Water Chilling Units are set up: the EIR performance curve (Energy Input to Cooling Output Ratio Function of Part Load Ratio Curve) of the temperature variant performance curve of refrigerating capacity (Cooling Capacity Function of Temperature Curve), the temperature variant performance curve of EIR (Energy Input to Cooling Output Ratio Function of Temperature Curve) and part load ratio.
Refrigerating capacity performance curve is defined as the refrigerating capacity factor and cooling water is intake, functional relation between chilled water leaving water temperature two independent variables, and the Mathematical Modeling of the refrigerating capacity factor is:
, wherein,
,
,
,
, and
it is the first fitting coefficient;
Namely EIR is defined as the inverse of COP, i.e. the ratio of wasted work rate and refrigerating capacity.The Mathematical Modeling of the first scale factor of wasted work rate and refrigerating capacity is:
, wherein
,
,
,
,
with
it is the second fitting coefficient; Under above formula illustrates any operating mode, the relation of cooling water, freezing temperature and unit COP.
As everyone knows, handpiece Water Chilling Units capacity is all determined by the load under design condition, and the handpiece Water Chilling Units most of the time is run all at part load, the adjusting function under unit sub-load is the important indicator of unit superiority-inferiority, is also related to the actual consumption of unit simultaneously.Under normal circumstances, adapted to the requirement of sub-load by the unlatching number of units reducing compressor, the adjustment of this method to unit is Nonlinear Adjustment, and in order to optimize conveniently, the Mathematical Modeling arranging the second scale factor of wasted work rate and refrigerating capacity is:
, wherein
,
with
be the 3rd fitting coefficient, PLR is the ratio of maximum cooling capacity under handpiece Water Chilling Units actual load and corresponding off-design behaviour.
Embodiment 5:
Based on an optimization system for the central air conditioner system of Mathematical Modeling, as shown in Figure 6, processing module 1 and scheduler module 2 is comprised; Processing module 1 is based on the energy consumption Mathematical Modeling set up, and in constraints, change the value of primary variables, the system total energy consumption of making reaches minimum of a value, and now, the value of systematic parameter is optimum operating condition value; Scheduler module 2 is for being adjusted to optimum operating condition value by primary variables.
Further, based on the above-mentioned Mathematical Modeling set up and primary variables, scheduler module 2 comprises handpiece Water Chilling Units module 21, water pump module 22, cooling tower module 23 and fan coil module 24, handpiece Water Chilling Units module 21 is for adjusting the variable of handpiece Water Chilling Units to described optimum operating condition value, water pump module 22 is for adjusting the variable of water pump to optimum operating condition value, cooling tower module 23 is for adjusting the variable of cooling tower to optimum operating condition value, and fan coil module 24 is for adjusting the variable of fan coil to optimum operating condition value.Wherein, in the present embodiment, for the cooling water leaving water temperature determined
, cold in-water temperature
, chilled-water flow
, the air quantity of fan coil under applying working condition
with the mass flow of air
these primary variables, chilled-water flow can regulate water pump by regulating frequency converter, and air quantity can by regulating the rotation speed of fan in fan coil adjusted, and water temperature regulates by changing handpiece Water Chilling Units desired temperature.
Further, processing module 1 comprises identification module 11, algoritic module 12 and computing module 13; Some derivation algorithms have been deposited in algoritic module 12.Identification module 12, for analyzing each energy consumption Mathematical Modeling, identifies mathematic(al) structure and the character of energy consumption Mathematical Modeling automatically; Computing module 13 is according to fixed mathematic(al) structure and character, just from algoritic module 12, choose the derivation algorithm corresponding with energy consumption Mathematical Modeling, the value of primary variables is changed in constraints, according to the derivation algorithm chosen, the numerical value of each primary variables when the system of trying to achieve reaches optimum power consumption state.In the present embodiment, the derivation algorithm that algoritic module 12 comprises has: (1) linear programming, and such optimization refers to that object function and constraints are linear function; (2) Non-Linear Programming, it refers in object function or constraints to there is nonlinear function; (3) quadratic programming, such is optimized essence and also belongs to Non-Linear Programming, and such optimization object function is quadratic function, and constraints is linear function; (4) integer programming, such variable optimized is all or part of integer, mainly comprises integral linear programming, Integral nonlinear program-ming, general integer programming etc.
If model is linear programming, computing module 13 next step solve directly calling linear solver; If model is Non-Linear Programming, computing module 13 next step solve directly calling non-linear solver; If model is integer programming, then computing module 13 next step will directly call integer programming solver, computing module 13 is mainly used in the branch-bound algorithm of managing integers planning problem, is in operation also constantly to call linear optimization solver and nonlinear optimization solver and carry out demarcation process.
As shown in Figure 7, in figure, curve 1, curve 2, curve 3, curve 4, curve 5 represent fan coil air quantity ratio respectively, the i.e. ratio of fan coil air quantity and standard air quantity, when it is respectively 1,0.9,0.8,0.7,0.6, system energy consumption is with freezing temperature variation relation, when air quantity ratio is less than 0.6, pump capacity exceeds maximum stream flow, and therefore this place does not consider.When can find out in figure that fan coil air quantity ratio equals 1,0.9,0.8,0.7, system energy consumption first declines with freezing temperature change and rises afterwards, has a system energy consumption minimum point in every bar curve.When air quantity ratio equals 0.6, system energy consumption increases with chilled water and increases progressively.Every bar curve is after freezing temperature reaches certain value, and it is 0 that system energy consumption lands vertically, this is because after in simulation process, the freezing water yield exceedes the maximum permissible flow of water pump, system energy consumption output valve is 0.Can find out 5 curve power consumption values contrasts, curve 3 is when namely freezing temperature is at about 5.6 DEG C for about about T=7s, whole system energy consumption is minimum, minimum of a value is approximately 152.6kW, this running operating point is optimum operating condition point, at 800kW load, test under the condition that air ' s wet bulb temperature is 26 DEG C, its optimum operating condition be chilled water 5.67 DEG C, cooling water 27.62 DEG C, fan coil air quantity than 0.78, system total energy consumption 152.86kW.Can find out that the value after optimization and simulation value are identical, illustrate that aforementioned optimum results precision is high, reach the target that energy consumption is minimum.
Above content is in conjunction with concrete embodiment further description made for the present invention, can not assert that specific embodiment of the invention is confined to these explanations.For general technical staff of the technical field of the invention, without departing from the inventive concept of the premise, some simple deduction or replace can also be made.
Claims (8)
1., based on a central air conditioner system optimization method for Mathematical Modeling, it is characterized in that, comprise the following steps:
Based on the energy consumption Mathematical Modeling set up, in constraints, change the value of primary variables, the system total energy consumption of making reaches minimum of a value, and now, the value of systematic parameter is optimum operating condition value;
Described primary variables is adjusted to described optimum operating condition value.
2. method according to claim 1, is characterized in that: described energy consumption Mathematical Modeling comprises:
The Mathematical Modeling of handpiece Water Chilling Units energy consumption:
Wherein,
for handpiece Water Chilling Units wasted work rate;
for the Energy Efficiency Ratio COP under handpiece Water Chilling Units declared working condition;
for the input power under handpiece Water Chilling Units declared working condition;
for the refrigerating capacity factor,
for the first scale factor of wasted work rate and refrigerating capacity,
for the second scale factor of wasted work rate and refrigerating capacity;
The Mathematical Modeling of pump energy consumption:
Wherein,
for pump energy consumption power;
for Media density;
for acceleration of gravity;
for water pump lift;
for the flow of water pump;
for the efficiency of frequency converter;
for the efficiency of motor;
for the efficiency of water pump;
Cooling tower energy consumption Mathematical Modeling:
Wherein,
for cooling tower energy consumption;
for identification coefficient;
for the mass flow of air;
for the mass flow of cooling water;
for cooling water leaving water temperature;
for cold in-water temperature;
Fan coil energy consumption Mathematical Modeling:
Wherein,
for the energy consumption of fan coil,
for the energy consumption of fan coil under standard condition;
for the discharge of fan coil under applying working condition,
for the discharge of fan coil under standard condition;
for the air quantity of fan coil under applying working condition,
for the air quantity of fan coil under standard condition;
for fan coil under applying working condition air intlet wet-bulb temperature,
for fan coil air intlet wet-bulb temperature under standard condition;
for fan coil under applying working condition chilled water inlet temperature,
for fan coil chilled water inlet temperature under standard condition;
System total energy consumption Mathematical Modeling:
=
+
+
+
, wherein
for system total energy consumption;
Described primary variables is cooling water leaving water temperature
, cold in-water temperature
, chilled-water flow
, the air quantity of fan coil under applying working condition
with the mass flow of air
Described constraints is:
,
for the wet-bulb temperature of surrounding air,
for the safety requirements value of condenser,
,
for the critical-temperature that cooling water freezes,
temperature for enough refrigeration dutys of meeting required for air-conditioned room and when meeting air conditioning area humidity;
,
for 40% of metered flow,
for metered flow;
,
for nominal air delivery;
,
for metered flow.
3. method according to claim 2, is characterized in that: the described energy consumption Mathematical Modeling based on having set up, and in constraints, change the value of primary variables, the step that the system total energy consumption of making reaches minimum of a value comprises:
Described energy consumption Mathematical Modeling is analyzed, the mathematic(al) structure of automatic identification energy consumption Mathematical Modeling and character, according to fixed mathematic(al) structure and character, choose the derivation algorithm corresponding to described energy consumption Mathematical Modeling, in constraints, change the value of primary variables, make system total energy consumption reach minimum of a value according to described derivation algorithm.
4. method according to claim 2, is characterized in that:
The Mathematical Modeling of the described refrigerating capacity factor is:
, wherein,
,
,
,
,
with
it is the first fitting coefficient;
The Mathematical Modeling of the first scale factor of described wasted work rate and refrigerating capacity is:
, wherein
,
,
,
,
with
it is the second fitting coefficient;
The Mathematical Modeling of the second scale factor of described wasted work rate and refrigerating capacity is:
, wherein
,
with
be the 3rd fitting coefficient, PLR is the ratio of maximum cooling capacity under handpiece Water Chilling Units actual load and corresponding off-design behaviour.
5. based on an optimization system for the central air conditioner system of Mathematical Modeling, it is characterized in that: comprise processing module and scheduler module; Described processing module is based on the energy consumption Mathematical Modeling set up, and in constraints, change the value of primary variables, the system total energy consumption of making reaches minimum of a value, and now, the value of systematic parameter is optimum operating condition value; Described scheduler module is used for described primary variables to be adjusted to described optimum operating condition value.
6. system according to claim 5, is characterized in that: described Mathematical Modeling comprises:
The Mathematical Modeling of handpiece Water Chilling Units energy consumption:
Wherein,
for handpiece Water Chilling Units wasted work rate;
for the Energy Efficiency Ratio COP under handpiece Water Chilling Units declared working condition;
for the input power under handpiece Water Chilling Units declared working condition;
for the refrigerating capacity factor,
for the first scale factor of wasted work rate and refrigerating capacity,
for the second scale factor of wasted work rate and refrigerating capacity;
The Mathematical Modeling of pump energy consumption:
Wherein,
for pump energy consumption power;
for Media density;
for acceleration of gravity;
for water pump lift;
for the flow of water pump;
for the efficiency of frequency converter;
for the efficiency of motor;
for the efficiency of water pump;
Cooling tower energy consumption Mathematical Modeling:
Wherein,
for cooling tower energy consumption;
for identification coefficient;
for the mass flow of air;
for the mass flow of cooling water;
for cooling water leaving water temperature;
for cold in-water temperature;
Fan coil energy consumption Mathematical Modeling:
Wherein,
for the energy consumption of fan coil,
for the energy consumption of fan coil under standard condition;
for the discharge of fan coil under applying working condition,
for the discharge of fan coil under standard condition;
for the air quantity of fan coil under applying working condition,
for the air quantity of fan coil under standard condition;
for fan coil under applying working condition air intlet wet-bulb temperature, be fan coil air intlet wet-bulb temperature under standard condition;
for fan coil under applying working condition chilled water inlet temperature,
for fan coil chilled water inlet temperature under standard condition;
With system total energy consumption Mathematical Modeling:
=
+
+
+
, wherein
for system total energy consumption;
Described primary variables is cooling water leaving water temperature, cold in-water temperature
, chilled-water flow
, the air quantity of fan coil under applying working condition
with the mass flow of air
Described constraints is:
,
for the wet-bulb temperature of surrounding air,
for the safety requirements value of condenser,
,
for the critical-temperature that cooling water freezes,
temperature for enough refrigeration dutys of meeting required for air-conditioned room and when meeting air conditioning area humidity;
,
for 40% of metered flow,
for metered flow;
,
for nominal air delivery;
,
for metered flow;
Described scheduler module comprises handpiece Water Chilling Units module, water pump module, cooling tower module and fan coil module, described handpiece Water Chilling Units module is for adjusting the variable of handpiece Water Chilling Units to described optimum operating condition value, described water pump module is for adjusting the variable of water pump to described optimum operating condition value, described cooling tower module is for adjusting the variable of cooling tower to described optimum operating condition value, and described fan coil module is for adjusting the variable of fan coil to described optimum operating condition value.
7. system according to claim 6, is characterized in that: described processing module comprises identification module, algoritic module and computing module; Described algoritic module has deposited some derivation algorithms; Described identification module is used for analyzing described energy consumption Mathematical Modeling, automatically identifies mathematic(al) structure and the character of energy consumption Mathematical Modeling; Described computing module is according to fixed mathematic(al) structure and character, the derivation algorithm corresponding with described energy consumption Mathematical Modeling is chosen in described algoritic module, in constraints, change the value of primary variables, make system total energy consumption reach minimum of a value according to described derivation algorithm.
8. system according to claim 6, is characterized in that:
The Mathematical Modeling of the described refrigerating capacity factor is:
, wherein,
,
,
,
,
with
it is the first fitting coefficient;
The Mathematical Modeling of the first scale factor of described wasted work rate and refrigerating capacity is:
, wherein
,
,
,
,
with
it is the second fitting coefficient;
The Mathematical Modeling of the second scale factor of described wasted work rate and refrigerating capacity is:
, wherein
,
with
be the 3rd fitting coefficient, PLR is the ratio of maximum cooling capacity under handpiece Water Chilling Units actual load and corresponding off-design behaviour.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510082203.4A CN104713197A (en) | 2015-02-15 | 2015-02-15 | Central air conditioning system optimizing method and system based on mathematic model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510082203.4A CN104713197A (en) | 2015-02-15 | 2015-02-15 | Central air conditioning system optimizing method and system based on mathematic model |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN104713197A true CN104713197A (en) | 2015-06-17 |
Family
ID=53412748
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510082203.4A Pending CN104713197A (en) | 2015-02-15 | 2015-02-15 | Central air conditioning system optimizing method and system based on mathematic model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104713197A (en) |
Cited By (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105004015A (en) * | 2015-08-25 | 2015-10-28 | 东南大学 | Central air-conditioning modeling and controlling strategy on basis of demand response |
| CN105115100A (en) * | 2015-07-22 | 2015-12-02 | 李振宇 | Central air conditioning equipment control system and method based on intelligent optimization |
| CN106529021A (en) * | 2016-11-09 | 2017-03-22 | 东南大学 | Air conditioning system simulation method based on feature recognition |
| CN106524295A (en) * | 2016-11-21 | 2017-03-22 | 北京建筑技术发展有限责任公司 | Regional building energy consumption predicting method |
| CN107044710A (en) * | 2016-12-26 | 2017-08-15 | 深圳达实智能股份有限公司 | Energy-saving control method for central air conditioner and system based on joint intelligent algorithm |
| CN107152760A (en) * | 2017-05-25 | 2017-09-12 | 杜艳林 | A kind of monitoring method and monitoring system of the health status of air-conditioning |
| CN107273673A (en) * | 2017-06-01 | 2017-10-20 | 国网江苏省电力公司经济技术研究院 | A kind of air conditioner cold water unit group control method for considering to stabilize wind-powered electricity generation fluctuation |
| CN107490985A (en) * | 2017-05-18 | 2017-12-19 | 宝沃汽车（中国）有限公司 | Body workshop energy management method and system |
| CN107560086A (en) * | 2017-09-19 | 2018-01-09 | 广东美的暖通设备有限公司 | Control method, device and the central air-conditioning of central air conditioning cooling water system |
| CN108846144A (en) * | 2018-03-30 | 2018-11-20 | 同济大学 | A kind of piping flow online test method independent of flowmeter |
| CN109425176A (en) * | 2017-08-31 | 2019-03-05 | 台湾积体电路制造股份有限公司 | The cooling system of optimal control and the autocontrol method of cooling system |
| CN109556236A (en) * | 2018-10-22 | 2019-04-02 | 珠海格力电器股份有限公司 | Energy efficiency controlling method and device |
| WO2019143482A1 (en) * | 2018-01-22 | 2019-07-25 | Siemens Industry, Inc. | System and method for optimizing performance of chiller water plant operations |
| CN111125933A (en) * | 2020-01-02 | 2020-05-08 | 珠海格力电器股份有限公司 | Correction method and system for simulation model of central air conditioner |
| CN111256313A (en) * | 2020-01-21 | 2020-06-09 | 田雨辰 | Intelligent refrigeration quality adjusting algorithm |
| CN111383719A (en) * | 2019-12-23 | 2020-07-07 | 浙江中控技术股份有限公司 | Hydrogen and gas collaborative optimization method used in oil refining process |
| CN111397262A (en) * | 2020-03-20 | 2020-07-10 | 浙江中控技术股份有限公司 | Control method and device of refrigeration system |
| CN111486552A (en) * | 2020-04-24 | 2020-08-04 | 辽宁工程技术大学 | Method for identifying water supply temperature strategy of chilled water of air conditioner based on subentry metering data |
| CN111895626A (en) * | 2020-07-06 | 2020-11-06 | 山东格瑞德集团有限公司 | Energy-saving control method based on central air conditioner of Internet of things |
| CN112105233A (en) * | 2020-09-21 | 2020-12-18 | 北京百度网讯科技有限公司 | Energy-saving control method and device, electronic equipment and computer readable medium |
| CN113158355A (en) * | 2021-01-29 | 2021-07-23 | 西安交通大学 | Low-temperature liquid expander full-working-condition optimization design method |
| CN113515093A (en) * | 2020-04-10 | 2021-10-19 | 阿里巴巴集团控股有限公司 | Data processing method, data processing device, production control method, production control device, equipment and storage medium |
| CN113865014A (en) * | 2021-09-14 | 2021-12-31 | 浙江中控技术股份有限公司 | Energy consumption coordination optimization method, device and equipment for large-scale cold water air-conditioning system |
| CN114001438A (en) * | 2021-09-15 | 2022-02-01 | 上海建工集团股份有限公司 | Temperature difference identification method for supply water and return water of air conditioning system |
| CN115031356A (en) * | 2022-06-30 | 2022-09-09 | 深圳市英维克科技股份有限公司 | Variable frequency air conditioner control method and device, electronic equipment and medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101363653A (en) * | 2008-08-22 | 2009-02-11 | 日滔贸易（上海）有限公司 | Energy consumption control method and device of central air-conditioning refrigeration system |
| CN102147146A (en) * | 2011-04-22 | 2011-08-10 | 黄真银 | Digital integrated intelligent control system of central air conditioner and adjusting method thereof |
| CN102322671A (en) * | 2011-07-29 | 2012-01-18 | 天津大学 | Central air-conditioning control method |
| CN103062861A (en) * | 2012-12-05 | 2013-04-24 | 四川长虹电器股份有限公司 | Energy-saving method and system for central air conditioner |
| CN104315673A (en) * | 2014-09-16 | 2015-01-28 | 珠海格力电器股份有限公司 | Central air conditioning fuzzy control system and control method |
-
2015
- 2015-02-15 CN CN201510082203.4A patent/CN104713197A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101363653A (en) * | 2008-08-22 | 2009-02-11 | 日滔贸易（上海）有限公司 | Energy consumption control method and device of central air-conditioning refrigeration system |
| CN102147146A (en) * | 2011-04-22 | 2011-08-10 | 黄真银 | Digital integrated intelligent control system of central air conditioner and adjusting method thereof |
| CN102322671A (en) * | 2011-07-29 | 2012-01-18 | 天津大学 | Central air-conditioning control method |
| CN103062861A (en) * | 2012-12-05 | 2013-04-24 | 四川长虹电器股份有限公司 | Energy-saving method and system for central air conditioner |
| CN104315673A (en) * | 2014-09-16 | 2015-01-28 | 珠海格力电器股份有限公司 | Central air conditioning fuzzy control system and control method |
Non-Patent Citations (2)
| Title |
|---|
| GUANG-YU JIN等: "A simplified modeling of mechanical cooling tower for control and optimization of HVAC systems", 《ENERGY CONVERSION AND MANAGEMENT》 * |
| 黄艳山: "空调水系统动态优化仿真研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑 2013年第02期》 * |
Cited By (38)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105115100B (en) * | 2015-07-22 | 2019-05-14 | 李振宇 | Central air conditioning equipment control system and method based on intelligent optimization |
| CN105115100A (en) * | 2015-07-22 | 2015-12-02 | 李振宇 | Central air conditioning equipment control system and method based on intelligent optimization |
| CN105004015B (en) * | 2015-08-25 | 2017-07-28 | 东南大学 | A kind of central air-conditioner control method based on demand response |
| CN105004015A (en) * | 2015-08-25 | 2015-10-28 | 东南大学 | Central air-conditioning modeling and controlling strategy on basis of demand response |
| CN106529021A (en) * | 2016-11-09 | 2017-03-22 | 东南大学 | Air conditioning system simulation method based on feature recognition |
| CN106529021B (en) * | 2016-11-09 | 2019-07-26 | 东南大学 | A kind of air-conditioning system simulation method based on feature identification |
| CN106524295A (en) * | 2016-11-21 | 2017-03-22 | 北京建筑技术发展有限责任公司 | Regional building energy consumption predicting method |
| CN107044710A (en) * | 2016-12-26 | 2017-08-15 | 深圳达实智能股份有限公司 | Energy-saving control method for central air conditioner and system based on joint intelligent algorithm |
| CN107490985A (en) * | 2017-05-18 | 2017-12-19 | 宝沃汽车（中国）有限公司 | Body workshop energy management method and system |
| CN107152760A (en) * | 2017-05-25 | 2017-09-12 | 杜艳林 | A kind of monitoring method and monitoring system of the health status of air-conditioning |
| CN107273673B (en) * | 2017-06-01 | 2020-03-17 | 国网江苏省电力公司经济技术研究院 | Air conditioner water chiller group control method considering stabilizing wind power fluctuation |
| CN107273673A (en) * | 2017-06-01 | 2017-10-20 | 国网江苏省电力公司经济技术研究院 | A kind of air conditioner cold water unit group control method for considering to stabilize wind-powered electricity generation fluctuation |
| CN109425176A (en) * | 2017-08-31 | 2019-03-05 | 台湾积体电路制造股份有限公司 | The cooling system of optimal control and the autocontrol method of cooling system |
| CN107560086A (en) * | 2017-09-19 | 2018-01-09 | 广东美的暖通设备有限公司 | Control method, device and the central air-conditioning of central air conditioning cooling water system |
| CN107560086B (en) * | 2017-09-19 | 2020-06-30 | 广东美的暖通设备有限公司 | Control method and device for central air conditioner cooling water system and central air conditioner |
| WO2019143482A1 (en) * | 2018-01-22 | 2019-07-25 | Siemens Industry, Inc. | System and method for optimizing performance of chiller water plant operations |
| CN108846144B (en) * | 2018-03-30 | 2021-10-08 | 同济大学 | Pipeline flow online detection method independent of flowmeter |
| CN108846144A (en) * | 2018-03-30 | 2018-11-20 | 同济大学 | A kind of piping flow online test method independent of flowmeter |
| CN109556236A (en) * | 2018-10-22 | 2019-04-02 | 珠海格力电器股份有限公司 | Energy efficiency controlling method and device |
| CN111383719A (en) * | 2019-12-23 | 2020-07-07 | 浙江中控技术股份有限公司 | Hydrogen and gas collaborative optimization method used in oil refining process |
| CN111383719B (en) * | 2019-12-23 | 2023-10-20 | 浙江中控技术股份有限公司 | Synergistic optimization method for hydrogen and gas in oil refining process |
| CN111125933A (en) * | 2020-01-02 | 2020-05-08 | 珠海格力电器股份有限公司 | Correction method and system for simulation model of central air conditioner |
| CN111256313A (en) * | 2020-01-21 | 2020-06-09 | 田雨辰 | Intelligent refrigeration quality adjusting algorithm |
| CN111256313B (en) * | 2020-01-21 | 2021-08-03 | 田雨辰 | Intelligent refrigeration quality adjusting algorithm |
| CN111397262A (en) * | 2020-03-20 | 2020-07-10 | 浙江中控技术股份有限公司 | Control method and device of refrigeration system |
| CN113515093A (en) * | 2020-04-10 | 2021-10-19 | 阿里巴巴集团控股有限公司 | Data processing method, data processing device, production control method, production control device, equipment and storage medium |
| CN111486552B (en) * | 2020-04-24 | 2021-07-20 | 辽宁工程技术大学 | Method for identifying water supply temperature strategy of chilled water of air conditioner based on subentry metering data |
| CN111486552A (en) * | 2020-04-24 | 2020-08-04 | 辽宁工程技术大学 | Method for identifying water supply temperature strategy of chilled water of air conditioner based on subentry metering data |
| CN111895626B (en) * | 2020-07-06 | 2023-01-24 | 山东格瑞德集团有限公司 | Energy-saving control method based on central air conditioner of Internet of things |
| CN111895626A (en) * | 2020-07-06 | 2020-11-06 | 山东格瑞德集团有限公司 | Energy-saving control method based on central air conditioner of Internet of things |
| CN112105233A (en) * | 2020-09-21 | 2020-12-18 | 北京百度网讯科技有限公司 | Energy-saving control method and device, electronic equipment and computer readable medium |
| CN112105233B (en) * | 2020-09-21 | 2023-05-12 | 北京百度网讯科技有限公司 | Energy saving control method, device, electronic equipment and computer readable medium |
| CN113158355A (en) * | 2021-01-29 | 2021-07-23 | 西安交通大学 | Low-temperature liquid expander full-working-condition optimization design method |
| CN113158355B (en) * | 2021-01-29 | 2022-10-25 | 西安交通大学 | All-condition optimization design method for low-temperature liquid expander |
| CN113865014A (en) * | 2021-09-14 | 2021-12-31 | 浙江中控技术股份有限公司 | Energy consumption coordination optimization method, device and equipment for large-scale cold water air-conditioning system |
| CN114001438A (en) * | 2021-09-15 | 2022-02-01 | 上海建工集团股份有限公司 | Temperature difference identification method for supply water and return water of air conditioning system |
| CN115031356A (en) * | 2022-06-30 | 2022-09-09 | 深圳市英维克科技股份有限公司 | Variable frequency air conditioner control method and device, electronic equipment and medium |
| CN115031356B (en) * | 2022-06-30 | 2024-04-12 | 深圳市英维克科技股份有限公司 | Variable frequency air conditioner control method, device, electronic equipment and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104713197A (en) | Central air conditioning system optimizing method and system based on mathematic model | |
| CN111237989B (en) | Building ventilation air conditioner control method and device based on load prediction | |
| CN104134100B (en) | A kind of energy-saving management system based on cloud computing | |
| WO2023030522A1 (en) | Data center air conditioning system diagnosis method and apparatus | |
| US20110190946A1 (en) | Method And System Of Energy-Efficient Control For Central Chiller Plant Systems | |
| CN110107989A (en) | Small-sized based on chilled water return water temperature optimum set point determines frequency water cooler and becomes temperature control method of water | |
| CN110895016A (en) | Fuzzy self-adaptive based energy-saving group control method for central air-conditioning system | |
| CN104515271B (en) | The optimal control for energy saving system and its control method of central air conditioning freezing station system | |
| CN108320042B (en) | Optimization method and device for circulating water system | |
| CN102213475A (en) | Adaptive management method for power consumption of data center | |
| CN104534627A (en) | Comprehensive efficiency control method of central air-conditioning cooling water system | |
| CN105546745A (en) | Group control method and device for central air conditioner main machine unit | |
| CN104121666A (en) | Hourly frequency conversion air supplying system and control method for central air conditioning ventilation system | |
| CN104236020B (en) | Method and device for controlling air conditioning system | |
| CN1186572C (en) | Variable pressure difference and variable flow rate control method and system for air conditioning water system | |
| CN205807750U (en) | Cold group control energy-saving control system and air-conditioning equipment | |
| CN104315683A (en) | Fuzzy PID (Proportion Integration Differentiation)-based water-cooled air conditioner regulation and control method | |
| JP2011043306A (en) | Energy saving air conditioning control system | |
| CN212720195U (en) | Cooling water system control device based on system overall energy efficiency ratio COP is best | |
| CN114440409A (en) | Self-adaptive energy-saving control method for central air-conditioning system | |
| CN113701321A (en) | Energy-saving frequency conversion control method for central air-conditioning water pump | |
| CN203258800U (en) | Central air conditioner dynamic tracking energy-saving management control system | |
| CN115860445B (en) | Multi-level collaborative scheduling method for uranium enrichment refrigerating unit and closed cooling tower | |
| JP2011226680A (en) | Cooling water producing facility | |
| CN204987368U (en) | Refrigerated water pump package energy -saving control system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20150617 |